Orthogonal Frequency Division Multiplexing (OFDM) has been adopted by various wireless standards such as IEEE 802.11a, 802.16, ETSI HIPERLAN/2 as well as digital video broadcasting (DVB). Channel estimation in OFDM systems involves channels that vary across the frequency domain sub-carriers and also across OFDM symbols in time. Further, the pilot channels and reference symbols are transmitted sparsely in time and frequency. The channel at all other locations must be estimated using the channel statistics in time and frequency and the channel obtained on pilot locations.
The 3rd Generation Partnership Project, Long Term Evolution (3GPP LTE) standards provide exemplary arrangements of reference symbol (“RS” or “R”) on a given OFDM time-frequency resource. More particularly, various reference symbol locations are defined in 3GPP TS 36.211, 3GPP Technical Specification for Physical Channels and Modulation, Section 5.6.1.2, “Physical resource mapping” (March 2007) which is incorporated by reference herein.
Techniques exist for channel estimation in OFDM systems based on scattered pilots, and channel estimators have been designed for various wireless standards such as IEEE802.16/WiMAX, 3GPP LTE, 3GPP2 Rev-C, IEEE 802.11, IEEE 802.20. Most of the corresponding receiver implementations, choose a frequency-domain filtering approach, such as Minimum Mean Square Error (MMSE), in which the channel at each subcarrier location is obtained after applying a filter over several neighboring pilot/reference symbols.
The MMSE filters are different for different subcarrier locations and for different operational Signal-to-Noise Ratio (SNR) conditions. They are also often pre-computed based on an assumed power-delay-profile (PDP). The most commonly applied PDP is the so-called “uniform PDP” which has a width in time equaling the duration of the entire OFDM cyclic prefix (CP).
However, if the pilot subcarriers are uniformly distributed with a spacing smaller than the coherence bandwidth, the so-called “DFT-based” channel estimator becomes feasible in which the IDFT operation on the pilots yields a time-domain channel. A DFT operation is then applied to convert the time-domain channel back to the frequency domain with a frequency resolution finer than the original pilot subcarrier sampling.
Since this type of estimator allows advanced processing of the time-domain channel based on an estimate of the actual PDP, it can provide significant performance gain to that of MMSE thanks to its superior noise suppression capability, especially at low SNRs which is critical for control channel coverage.
Unfortunately, at high SNR, existing “DFT-based” estimators are inferior to MMSE estimators for various reasons. For example an inherent aliasing issue exists. Just like limited time-domain sampling and windowing in the time domain will cause aliasing in the frequency domain, the time-domain channel after IDFT operation on the pilots will suffer from “temporal” aliasing, which exhibits itself as the leakage of power from the true channel tap to its neighboring taps.
Further, in some cases the spacing between pilot subcarriers may not always be maintained. For example, since the “DC subcarrier” is typically not usable for data or pilot in OFDM, it is often not counted when assigning pilots. As a result of DC exclusion, the pilots before and after the DC subcarrier will be one more subcarrier further apart. Special treatment is needed to address this issue.
Perhaps more importantly, a typical OFDM system employs guard subcarriers which are not usable for the system. Such a “windowing” in the frequency domain not only causes aliasing as discussed previously, but also creates an “edge effect” on channel estimation quality for subcarriers near both edges. DFT-based estimators suffer more performance degradation from this “edge effect” than MMSE, because DFT-based estimators enforce an artificial correlation between the two edges as if the spectrum is “wrap around,” which is not true in a real channel. Techniques to further reduce the edge effect are thus needed.
In light of the above discussion, at low SNRs, the channel estimator should have good noise suppression, while at high SNRs, the channel estimator should have low interpolation errors, to avoid the capping of achievable SNR due to irreducible interpolation errors.
Therefore, an apparatus and method for channel estimation that can achieve the best performance under both low and high SNR conditions is needed. Furthermore, a channel estimation apparatus and method is needed wherein various interference characteristics are accounted for such that high performance may be obtained in interference-dominated or colored-noise environments such as those that may occur in an OFDM system.